Learning Transfer at Skill Institutions’ and Workplace Environment: A Conceptual Framework
Bibliographic record
Abstract
Efficient human resource management and skills development are central to any organization. However, identified less than 15 to 20 percent of the knowledge and skills acquired in trainings were actually applied in workplaces. Lack of awareness and limited skills learned have caused loss of funds invested in training programs and continued to contribute to mismatches in labour issues. Thus, this conceptual paper proposes key aspects of learning transfer required in training institution based on National Occupational Skill Standards (NOSS) system and in workplace environment. A conceptual framework which is based on critical reviews of current approaches in studies of learning transfer has been devised to highlight the relationship between learning transfer and skills training for today’s workplaces. The framework is a scientifically robust framework for transfer of learning at skill institutions. This study is significant in emphasizing the need for appropriate evaluation methods that can assist practitioners at skill institutions to develop learning transfer in a more credible manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".